Atmospheric data assimilation is the process of using imperfect observations of the atmosphere and imperfect computer models to estimate the past evolution of the atmosphere.

These records of past atmospheric states produced by data assimilation are used to study climate, detect model errors and initialise weather forecasts. Many of the largest improvements in weather forecasting over the last decade have been due to improvements in data assimilation techniques.

A key component of any data assimilation technique is a description of the inaccuracy of short-term forecasts. An earlier paper (Bishop et al., 2017) showed how to improve the description of this inaccuracy in a computationally efficient way.

This paper investigates whether the improvement suggested by Bishop et al. would lead to weather forecast improvements by implementing Bishop et al’s idea in a data assimilation scheme used operationally by the United States Weather Service.

The paper finds that Bishop et al’s new method led to large improvements in forecast accuracy when satellite observations of electromagnetic radiation emanating from the Earth were used to inform the data assimilation scheme.

  • Paper: Bishop CH, Whitaker JS, Lei L, 2017: Gain form of the Ensemble Transform Kalman Filter and its relevance to satellite data assimilation with model space ensemble covariance localization. Mon. Wea. Rev., 145, 4575–4592.